Enhanced ecg workflows

ABSTRACT

A method and system for obtaining and analyzing ECG waveforms from a patient is disclosed. Initially, a recommendation for obtaining an ECG waveform from a patient is issued, typically from a cardiologist. An ECG recording device is used to obtain ECG data from the patient. The recording device generates patient identification information, time and date information and an initial analysis that are included in a log delivered along with the ECG data to an analysis server. The analysis server generates a recommended action for the patient, which is delivered to the recording device while the patient is still present at the recording device. The recording device can then carry out the recommended action and provide the results for analysis by the cardiologist. The analysis server can utilize artificial intelligence/machine learning to generate the recommended action for the patient without having to involve the cardiologist, thereby reducing the number of visits for the treatment of the patient.

CROSS-REFERENCE TO RELATED APPLICATION

The present application is a continuation of U.S. patent applicationSer. No. 16/820,908, filed on Mar. 17, 2020 at the United States Patentand Trademark Office. The entire contents of the above-referencedapplication are hereby incorporated by reference for all purposes.

BACKGROUND

The present disclosure generally relates to improvements in workflowsdone in the analysis of ECG waveforms obtained from a patient. Morespecifically, the present disclosure relates to enhancement of the ECGworkflow by doing analysis of user actions on a recording device foreach of the patients and providing recommendation for any furtheranalysis while the patient is present at the recording device to reducetime for early treatment plans.

BRIEF DISCLOSURE

Presently, the typical ECG workflows are done offline based on therecommendation of a cardiologist. The typical workflow includes theinitial step of the patient detecting some problem and going to acardiologist for a visit. After initially seeing the cardiologist, thecardiologist typically recommends a 10 sec resting ECG for initialanalysis. After the resting ECG is recommended for the patient at theinitial visit with the cardiologist, the patient goes to a diagnosticcenter (either the same hospital or a separate diagnostic center like indeveloping countries) to take the ECG reading.

At the diagnostic center, a trained technician takes the ECGmeasurements from the patient and gives the measurements to the patientor updates ECG measurement information online. After receiving themeasurements, the patient typically returns to the cardiologist and thecardiologist checks for ECG abnormalities. If the cardiologist observessome type of abnormality in the ECG, such as an arrhythmia, thecardiologist recommends further analysis for the patient. For example,the cardiologist may recommend a “Hi-Res”/“30 min Of Rhythm”/“StressECG” for the patient.

As a result of this analysis of the initial ECG measurement, the patientmust return to the technician and get the cardiologist recommended ECGtaken. After this second ECG, the patient again returns to thecardiologist and the cardiologist reviews the second ECG and recommendstreatment for the patient.

The inventors of the present disclosure have identified problems withthis current workflow and have developed the present disclosure toenhance and improve on the state of the art. The present disclosuredescribes enhancement of this workflow by doing analysis of user actionson a recording device for patients and provide recommendation for anyfurther analysis while the patient is still at the recording device toreduce time for treatment.

The enhanced workflow in accordance with the present disclosure includesthe initial step of the patient detecting some problem and going to thecardiologist. The cardiologist will then typically recommend a 10 secresting ECG for initial analysis. As in the past workflow set forthabove, the patient then goes to a diagnostic center (either the samehospital or a diagnostic center) to take ECG reading.

After the initial ECG reading is obtained from the patient, the initialECG reading from the patient is analyzed either at the recording deviceor by a remote serve and the technician received a recommendation whilethe patient is still present at the ECG recording device. For example,the recommendation may be to further take “30 minrhythm”/“Hi-Res”/“Stress ECG”. The recommendation could include otherECG recording formats depending on the patient and the analysis. Sincethis recommendation is received while the patient is still present atthe recording device, the technician can obtain the enhanced ECGrecording and can give a full, detailed recording to the patient or canupload the information to a data network.

After leaving the diagnostic center, the patient can visit acardiologist and the cardiologist can review the enhanced ECG waveformfor abnormalities and can recommend treatment. Such enhanced workfloweliminates the intermediate visits with the cardiologist as in the priorart workflow described above.

As a result of the present disclosure, the system and method can developan early treatment plan for the patient as compared to the workflow ofthe prior art. This reduction in time is due to the elimination of theback and forth between the ECG technician and the cardiologist. Further,a proper detailed ECG can be recorded the first time the patient meetswith the ECG technician, even if the technician has less expertise andexperience, which is common in diagnostic center. Finally, the presentdisclosure provides enhanced analysis by the combination of multiple ECGrecording workflow is made possible.

BRIEF DESCRIPTION OF THE DRAWINGS

FIG. 1 is a flow chart depicting a prior art workflow in obtaining ECGand analyzing ECG waveforms from a patient;

FIG. 2 is a schematic diagram of a ECG workflow in accordance with anexemplary embodiment of the present disclosure;

FIG. 3 is a schematic diagram of the receipt of data from multiplepatients and the conversion of the data to a form for further processingand analysis;

FIG. 4 is a flow chart that depicts an exemplary embodiment of aworkflow for the analysis of ECG data obtained from a patient utilizingthe analysis method of the present disclosure;

FIG. 5 is a schematic diagram showing the use of artificialintelligence/machine learning in generating a next action based on priorworkflows for other patients;

FIG. 6 is a schematic diagram illustrating the types of ECG waveformsand setting that can be configured on the recording device based on therecommended action determined in accordance with the exemplaryembodiment; and

FIG. 7 is a schematic illustration of the types of ECG analysisavailable for use in carrying out the recommended action.

DETAILED DISCLOSURE

FIG. 1 illustrates the prior art, typical workflow done in obtaining ECGwaveforms from a patient and the steps necessary to perform the properanalysis of the ECG waveform by a cardiologist. As indicated in step 10,the patient and/or health care professional initially detects some sortof health problem and travels to an initial visit with a cardiologist.Initially, in step 20, the cardiologist meets with the patient and,based upon the initial meeting, suggests an initial ECG recording, suchas a ten second resting ECG, in step 12. The ten second resting ECG canbe used by the cardiologist to initially provide limited diagnosticinformation relative to cardiac health of the patient.

After the cardiologist suggests the resting ECG in step 12, the patienttravels to a diagnostic center, which may be the same hospital where thepatient met with the cardiologist or may be a separate diagnosticcenter, as is the case in developing countries. After reaching thediagnostic center, as illustrated in step 14, a technician at thediagnostic center takes the ECG measurement from the patient in step 16.In many cases, the technician may be trained only on how to operate theECG recording device and may not be able to make any type of initialdiagnosis based on the ECG measurement. Such is the case in manydeveloping countries.

After the initial ECG measurement has been taken from the patient, thetechnician either provides the information to the patient in anelectronic form or uploads the recorded information to an electronicsite that is accessible by the cardiologist. After the initial ECGmeasurement has been taken in step 16, the patient returns to thecardiologist in step 18. While at the cardiologist for the second visit,the cardiologist can review the ECG recording and can check for ECGabnormalities in step 20. If no abnormalities are detected, thecardiologist provides this information to the patient in step 22 and theanalysis is complete.

However, if the cardiologist determines in step 20 that abnormalitiesare present in the ECG recording in step 20, the cardiologist recommendsthat further analysis be carried out and that additional ECG informationshould be obtained from the patient. As an example, the cardiologist mayrecommend that a Hi-Res ECG or a stress induced ECG be performed for thepatient. The recommendation is not limited to the exemplary report typesand could include any other ECG report. To get this type of testingdone, the patient returns to the diagnostic center, as illustrated bystep 24. At the diagnostic center, the recommended ECG for the patientis obtained by the trained technician as illustrated in step 26. Afterthis second, recommended ECG is taken, the ECG recording is again eithergiven to the patient or uploaded for viewing by the cardiologist. Afterthis second ECG recording is taken for the patient, the patient againreturns to the cardiologist for a third visit in step 26, where thecardiologist can then perform a more full and complete analysis of therecommended ECG that was taken in step 26.

As can be understood by the prior art workflow illustrated in FIG. 1 ,the patient typically needs to return to the diagnostic center andcardiologist multiple times depending upon whether the cardiologistdetects abnormalities in the initial ECG waveform following the initialECG reading. The inventors of the present disclosure have identified theproblems associated with the current workflow shown in FIG. 1 and havedeveloped the present disclosure to enhance and improve upon theworkflow shown in FIG. 1 .

FIG. 2 is a schematic illustration of the workflow 30 carried out inaccordance with the present disclosure. As illustrated in FIG. 2 ,multiple ECG recording devices 32 can be located at a diagnostic centerand each can be used to obtain ECG waveforms from a patient. Each of theECG recording devices 32 are commonly known ECG recording devices thatcan be utilized to obtain ECG waveforms from a patient. In FIG. 2 , theECG waveform data obtained from the patient is illustrated by the ECGinformation display 34. In accordance with the present disclosure, oncethe ECG waveform data has been obtained from the patient, each of theindividual ECG recording devices 32 can communicate the obtained ECGwaveform information to an analysis server 36. The ECG analysis server36 is typically located remotely from the individual recording devices32. For example, the analysis server 36 could be located in a differentarea of a hospital or diagnostic center from the individual recordingdevices. Alternatively, the analysis server 36 could be located at anyremote location and the ECG waveform data can be electronicallytransferred to the server 36 along the communication line 36. Thecommunication line 36 could be any type of communication network, suchas the internet or some hardwired communication technique. The analysisserver 36 includes internal processors that are able to carry out theanalysis that will be described below. In addition, the analysis server36 includes the required storage medium such that the ECG waveformsobtained from individual patients through the recording devices 32 canbe stored and analyzed as will also be described in detail below.

As illustrated in FIG. 2 , after the ECG waveform data has been obtainedfrom the recording device 32 at the analysis server, the analysis serverconverts the data into a usable format, as illustrated by block 40.Block 40 is further described with reference to FIG. 3 . As illustratedin FIG. 3 , in addition to the raw ECG waveform data obtained from therecording device, the analysis server also receives a log 42 thatprovides both a data/time stamp 44 and a description of the type ofaction taken on the patient, as illustrated the user action column 46.

As illustrated in the first row 48 of the log 42, an ECG was started forpatient ID number 1240 at 5:37:00. The user action is thus recordedalong with the time of the user action. In row 50, the log 42 indicatesthat the ECG recording was accepted for patient ID number 1240. Afterthe ECG is accepted, the ECG recording device provides an initialanalysis 52, which is saved in the user action column. The initialanalysis 52 stored in the user action column indicates that anarrhythmia is present in the ECG waveform data obtained from patient1240.

The recording device 32 can utilize one of multiple different types ofECG analysis algorithms, such as the proprietary 12SL algorithm utilizedby GE Healthcare. The initial analysis carried out by the recordingdevice is stored and transmitted with the ECG data, as indicated by row54 of the log 52. As can be seen in the first five rows of the log 42,the patient ID number 1240 has obtained an ECG reading, had the readinganalyzed by the recording device and the ECG waveform data and log dataare transmitted to the analysis server.

Once patient ID number 1240 has completed the initial ECG recording andanalysis, the recording device is then used on the next patient havingpatient ID number 1256 as indicated in row 56 of the log 42. The ECGrecording device obtains ECG information from patient ID number 1256,the ECG waveform is accepted in row 57 and the initial analysis isperformed by the recording device. In row 58, the initial analysisindicates that the ECG was normal and that the heartbeat for the patientwas 70 BPM, as indicated by the user action in row 58.

In row 60, the log 42 indicates that a Hi-Res ECG was performed for thepatient ID number 1240 discussed above. As can be understood by the timestamp column, the Hi-Res ECG for patient ID number 1240 occursapproximately thirteen minutes after the initial ECG was taken, asindicated by row 48. The Hi-Res ECG is a recommended action for thepatient based on the initial ECG and the recommended action determinedby the system and method of the present disclosure, as will be discussedin detail below.

As can be understood by the timing illustrated by the rows in the log42, the recommended action for the patient occurs very shortly after theinitial action. In such situation, the patient has not left the areanear the recording device and the second Hi-Res ECG can be obtainedwithout the patient having to visit the cardiologist. In row 62, therecording device again detects that an arrhythmia is present and thatthe Hi-Res ECG indicates flat or low amplitude T waves are present inthe ECG waveform. This analysis is recoded in row 62 and is transmitted,along with the ECG obtained in row 60, to a cardiologist, as indicatedby the user action shown in row 64.

As indicated in FIG. 3 , the log 42, which includes the date and timestamp as well as information related to the user action and any analysiscarried out by the recording device, is sent to the analysis serverutilizing the communication line 36. When the log 42 is received by theanalysis server, the analysis server carries out the function shown byblock 40. In this functional block, the data from the log 42 isconverted into a usable format. The usable format for the data containedwithin the log 42 is illustrated by the summary table 66.

The first column 68 on the summary table 66 provides a patientidentification number as was included in the user action column of thelog 42. The conversion block 40 pulls the patient identificationinformation form the log 42. The second column 70 provided for theinitial analysis that is generated by the operating algorithm on therecording device. As can be understood by the comparison between thesummary table 66 and the data log 42, patient 1256 had a normal ECG with70 beats per minute (BPM). Since the analysis of the ECG was normal, thesecond initial analysis column 72 is blank. However, for patient IDnumber 1240, the first initial analysis 70 was the presence of anarrhythmia and the second initial analysis 72 is the presence of flat orlow amplitude T waves. Based upon these two initial analyses, theanalysis server calculates the next recommended action for the patient,as shown by column 76. For example, based upon the identification of anarrhythmia with flat or low amplitude T waves, the next recommendedaction proposed is to record a Hi-Res ECG, as indicated by data block 78in the next action column 76. This next action indicated by block 78 isthen transmitted back to the recording device along data communicationline 80 shown in FIG. 2 .

As indicated by decision block 82 in FIG. 2 , after the recording device32 obtains an ECG recording from the patient, the recording device 32checks with the analysis server 36 for a recommendation based on the ECGthat was recorded. As indicated above, in the next action block 78 shownin FIG. 3 , the analysis server generated a recommended action that aHi-Res ECG is needed for the patient. Since the patient is still presentat the recording device 32, the recording device can display therecommended action to the technician in block 84. When the technicianreceives the directions shown on the recording device 32 in block 84,the technician can then obtain the required ECG from the patient. In theembodiment described, the technician would then obtain a Hi-Res ECG fromthe patient for further analysis by the cardiologist.

As illustrated in FIG. 2 , the analysis server 36 includes a functionalblock 90 that includes a storage device 92 for receiving and storingboth the ECG waveform data and the initial analysis information fromeach of the recording devices 32. The analysis block 90 further includesa machine learning algorithm 94 that is used as will be described indetail below to generate a recommend action for the patient based uponthe first and second analysis information provided from the recordingdevice and based upon prior stored recommendations from previouspatients that had similar ECG data and waveforms. The machine learningalgorithm 94 indicated by functional block 94 is further illustrated inFIG. 5 . The machine learning algorithm 94 shown in FIG. 5 utilizes boththe first analysis 70 and a second analysis 72 to create and generatethe next action 76. The artificial intelligence/machine learninganalysis illustrated by step 94 allows the workflow system and method ofthe present disclosure to generate a next action that can then berelayed to the technician at the ECG recording device such that thetechnician can carry out the next action on the patient.

FIG. 4 explains a basic example of the overall workflow in accordancewith the concepts of the present disclosure. As shown in FIG. 4 , theinitial step 100 records the ECG from the patient utilizing one of therecording devices 32. After the initial ECG is recorded, an ECG analysisis carried out in step 102. The ECG analysis carried out in step 102 iscarried out utilizing the algorithm present on the recording device 32.As an illustrated example, in block 102 the ECG analysis for therecorded ECG indicates the presents of an arrhythmia. The initialanalysis is then recorded in the log 42 shown in FIG. 3 for the patient,along with a date/time stamp.

The workflow in FIG. 4 proceeds to step 104 where the recording devicecommunicates the ECG waveform data and the initial analysis to theserver as part of the log. The communication of the ECG information andthe initial analysis to the analysis server allows the analysis serverto determine the next recommended patient action.

In step 106, the analysis server checks with user action analysiscomponent of the log to determine what should be the next recommendedaction for the patient. In step 108, the AI/ML analysis component 94utilizes machine learning along with the analyzed previous data fromearlier patients to determine what the recommended next action should befor the patient. In this manner, the machine learning/artificialintelligence algorithm is able to make a recommended action for thepatient that can be relayed to the recording device such that thetechnician can carry out the most relevant action for the patient whilethe patient is present at the recording device.

In the illustrated example shown in FIG. 4 , based upon the analysiscarried out in steps 106 and 108, the analysis server determines that afive minute rhythm/Hi-Res ECG is most beneficial for the patient foranalyzing the ECG. This analysis and recommended next action for thepatient is communicated to the recording device as indicated in step108. In step 110, the recommended patient action is displayed on therecording device and the technician is able to carry out the actionneeded. As indicated in detail below, the action taken by the techniciancan be anyone of multiple different types of ECG recordings for thepatient. Such recordings may include a Hi-Res recording, a 15-Leadrecording, a 12-Lead recording or any other type of known recordings ofECG information from a patient. Since the patient is still present atthe ECG recording device, the recommended action determined by theanalysis server can be carried out immediately while the patient ispresent without having the patient leave the diagnostic center for theanalysis and having to return for later ECG recordings.

FIG. 6 presents a graphic illustration of the different types ofinformation that is able to be recorded from a patient utilizing therecording device 32 shown in FIG. 1 . The initial display 150 includesvarious different menu logs that the technician can utilize whenrecording the ECG information from the patient. As indicated, the ECGLead set type shown in area 152 allows the technician to select betweenfive different types of ECG measurements indicated by menu block 154.Further, a sub block 156 allows the technician to utilized differentcombinations of leads applied to the patient.

Menu block 158 allows the technician to select between different speeds,gains, and filters for the ECG information recorded from the patient.Again, the technician can make selections in this menu block based uponthe type of ECG to be recorded from the patient.

Based upon the selections from the display 150, the technician can startthe recording process as indicated by arrow 160. The ECG waveforms arerecorded as shown by the recording block 162. Block 164 illustrates theanalysis that can be carried out on the recorded ECG waveform data. Aseparate report 166 can be generated depending upon the type ofinformation selected by the technician, as indicated by block 168. Sincethe device Logs contain information about ECG types, reports andsettings, an AI/ML recommendation system can utilize this information toprovide suggestions to the user about report types and settings to beused for recording the ECG from the patient.

FIG. 7 further illustrates the various types of ECG data that can beobtained from the patient utilizing the acquisition module 180 of therecording device. The acquisition model 180 obtains ECG information fromthe patient as illustrated by arrow 182. Some of the ECG data can beobtained from the same recorded ECG information by adjusting thefilter/calculation values utilizing the host module 184. The host module184 is thus able to process the ECG data obtained from the patient tocreate the processed ECG data 186. The processed ECG data 186 can beutilized to generate various different types of ECG data reports, suchas the full disclosure report 188, the Hi-Res report 190 and other typesof ECG data thus as illustrated in FIG. 7 . The report types shown inFIG. 7 are not limited to those shown and additional report types, suchas 12/15 leads stress can also be suggested based on the configurationof the recording device.

Citations to a number of references are made herein. The citedreferences are incorporated by reference herein in their entireties. Inthe event that there is an inconsistency between a definition of a termin the specification as compared to a definition of the term in a citedreference, the term should be interpreted based on the definition in thespecification.

In the above description, certain terms have been used for brevity,clarity, and understanding. No unnecessary limitations are to beinferred therefrom beyond the requirement of the prior art because suchterms are used for descriptive purposes and are intended to be broadlyconstrued. The different systems and method steps described herein maybe used alone or in combination with other systems and methods. It is tobe expected that various equivalents, alternatives and modifications arepossible within the scope of the appended claims.

The functional block diagrams, operational sequences, and flow diagramsprovided in the Figures are representative of exemplary architectures,environments, and methodologies for performing novel aspects of thedisclosure. While, for purposes of simplicity of explanation, themethodologies included herein may be in the form of a functionaldiagram, operational sequence, or flow diagram, and may be described asa series of acts, it is to be understood and appreciated that themethodologies are not limited by the order of acts, as some acts may, inaccordance therewith, occur in a different order and/or concurrentlywith other acts from that shown and described herein. For example, thoseskilled in the art will understand and appreciate that a methodology canalternatively be represented as a series of interrelated states orevents, such as in a state diagram. Moreover, not all acts illustratedin a methodology may be required for a novel implementation.

This written description uses examples to disclose the invention,including the best mode, and also to enable any person skilled in theart to make and use the invention. The patentable scope of the inventionis defined by the claims, and may include other examples that occur tothose skilled in the art. Such other examples are intended to be withinthe scope of the claims if they have structural elements that do notdiffer from the literal language of the claims, or if they includeequivalent structural elements with insubstantial differences from theliteral languages of the claims.

1. A system for assisting in obtaining ECG waveforms from a patientcomprising: an ECG recording device comprising: one or more electrodesconfigured to detect bioelectric impedance on a patient; a measuringdevice configured to receive raw data from the electrodes and generatean ECG waveform; and a transceiver configured to transmit and receivedata; an analysis server configured to analyze processed ECG data;wherein the ECG recording device is programmed to: sense an initial ECGwaveform from the patient using the ECG recording device in a first leadconfiguration; generate an initial analysis at the ECG recording devicebased on the ECG waveform obtained from the patient by the recordingdevice; communicate the recorded ECG waveform and the initial analysisfrom the recording device to the analysis server; receive a recommendedaction from the analysis server; and operate to carry out therecommended action on the patient; wherein the analysis server isprogrammed to: analyze the recorded ECG waveform and the initialanalysis; determine the recommended action; and communicate therecommended action to the recording device, wherein the recommendedaction comprises sensing a second ECG waveform using a different leadconfiguration than the first lead configuration.
 2. The method of claim1, wherein the different lead configuration comprises a configurationhaving different leads than the first lead configuration.
 3. The methodof claim 1, wherein the different lead configuration comprises aconfiguration having a different amount of leads than the first leadconfiguration.
 4. The method of claim 1, wherein the analysis server isprogrammed to determine the recommended action by: comparing theinitially sensed ECG waveform to previous patient ECG waveforms usingmachine learning; and generating the recommended action based on machinelearning analysis.
 5. The method of claim 1, wherein the recommendedaction is communicated to the recording device while the patient is atthe recording device.
 6. The method of claim 1, wherein the recommendedaction is determined based on previously determined recommended actions.7. The method of claim 6, wherein the recommended action is determinedbased on previously determined recommended actions associated with thepatient.
 8. A system for assisting in obtaining ECG waveforms from apatient, comprising: one or more processors; one or more memory storinginstructions; wherein the one or more processors are configured toexecute the instructions to: receive, from a recording device, aninitial ECG waveform sensed from the patient using a first leadconfiguration and an initial analysis of the ECG waveform; determine arecommended action based on at least one of the initially sensed ECGwaveform and the initial analysis; and transmit the recommended actionto the recording device; wherein the recommended action comprisessensing a second ECG waveform using a different lead configuration thanthe first lead configuration.
 9. The method of claim 8, wherein thedifferent lead configuration comprises a configuration having differentleads than the first lead configuration.
 10. The method of claim 8,wherein the different lead configuration comprises a configurationhaving a different amount of leads than the first lead configuration.11. The method of claim 8, wherein the one or more processors areconfigured to execute the instructions to determine the recommendedaction by: utilizing machine learning to compare the initially sensedECG waveform to previous patient ECG waveforms; and generating therecommended action based on machine learning analysis.
 12. The method ofclaim 8, wherein the recommended action is communicated to the recordingdevice while the patient is at the recording device.
 13. The method ofclaim 8, wherein the recommended action is determined based onpreviously determined recommended actions.
 14. An ECG recording systemfor assisting in obtaining ECG waveforms from a patient, comprising: oneor more processors; one or more memory storing instructions; one or moreelectrodes configured to detect bioelectric impedance; a transceiverconfigured to receive and transmit data; wherein the one or moreprocessors are configured to execute the instructions to: sense aninitial ECG waveform from the patient using the one or more electrodesin a first lead configuration; generate an initial analysis based on theinitial ECG waveform; transmit the initial ECG waveform and the initialanalysis to a server; and receive a recommended action from the server;wherein the recommended action comprises sensing a second ECG waveformusing a different lead configuration than the first lead configuration.15. The system of claim 14, wherein the different lead configurationcomprises a configuration having different leads than the first leadconfiguration.
 16. The system of claim 14, wherein the different leadconfiguration comprises a configuration having a different amount ofleads than the first lead configuration.
 17. The system of claim 14,wherein the one or more processors are configured to execute theinstructions to control a display to display information correspondingto the recommended action.